🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risk in terms of network, shell, and obfuscation activities but has a higher metadata risk due to its newness and lack of maintainer history or associated repository.

Metadata risk score is 4 out of 10
No associated GitHub repository

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires network interaction to function properly.
Shell: No shell execution patterns detected, indicating the package does not execute system commands.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
Credentials: No credential harvesting patterns detected, indicating low risk of secret theft.
Metadata: The package is newly uploaded with limited maintainer history and no associated GitHub repository, which raises some suspicion.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

⚠ Registered Email Domain score 3.0

Suspicious email domain flags: Very short email domain: cs.byu.edu

Very short email domain: cs.byu.edu

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

Only one version has ever been released — brand new package
Package is very new: uploaded 2 day(s) ago
Author "Gordon Bean" appears to have only 1 package on PyPI (new or inactive account)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with aarg-canvas

Create a Python-based mini-application named 'CanvasGradeBot' that integrates with the 'aarg-canvas' package to streamline grading processes for educators using the Canvas Learning Management System (LMS). This tool aims to simplify the feedback and grading workflow by allowing teachers to quickly input grades and comments directly from their terminal or command line interface.

Key Features:
1. Authentication: Implement user authentication to securely connect to a Canvas account using OAuth2 tokens provided by the 'aarg-canvas' package.
2. Grade Submission: Enable users to submit grades and comments for individual assignments. Users should be able to specify the course ID, assignment ID, student ID, and grade value.
3. Feedback Generation: Integrate a feature that allows the generation of automated feedback based on predefined templates. These templates can be customized by the user to fit specific grading criteria.
4. Reporting: Develop a reporting system that compiles statistics on grading activities, such as the number of assignments graded, average grade, and time spent grading.
5. CLI Interface: Design a Command Line Interface (CLI) that is intuitive and user-friendly, guiding users through each step of the grading process.

Utilization of 'aarg-canvas':
The 'aarg-canvas' package will serve as the backbone for interfacing with the Canvas API. It will handle authentication, data retrieval, and submission processes, ensuring that all operations comply with Canvas's API standards. Your task is to leverage this package to build a robust, efficient, and user-friendly tool that enhances the grading experience for educators.